Abstract
Lay summarization aims to generate lay summaries of scientific papers automatically. It is an essential task that can increase the relevance of science for all of society. In this paper, we build a lay summary generation system based on BART model. We leverage sentence labels as extra supervision signals to improve the performance of lay summarization. In the CL-LaySumm 2020 shared task, our model achieves 46.00 Rouge1-F1 score.- Anthology ID:
- 2020.sdp-1.35
- Volume:
- Proceedings of the First Workshop on Scholarly Document Processing
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Muthu Kumar Chandrasekaran, Anita de Waard, Guy Feigenblat, Dayne Freitag, Tirthankar Ghosal, Eduard Hovy, Petr Knoth, David Konopnicki, Philipp Mayr, Robert M. Patton, Michal Shmueli-Scheuer
- Venue:
- sdp
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 303–309
- Language:
- URL:
- https://preview.aclanthology.org/icon-24-ingestion/2020.sdp-1.35/
- DOI:
- 10.18653/v1/2020.sdp-1.35
- Cite (ACL):
- Tiezheng Yu, Dan Su, Wenliang Dai, and Pascale Fung. 2020. Dimsum @LaySumm 20. In Proceedings of the First Workshop on Scholarly Document Processing, pages 303–309, Online. Association for Computational Linguistics.
- Cite (Informal):
- Dimsum @LaySumm 20 (Yu et al., sdp 2020)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/2020.sdp-1.35.pdf
- Code
- TysonYu/Laysumm
- Data
- ScisummNet